As far as I understand that the Overall Performance represents the long term performance, while the observed performance represents the recorded performance. I noticed that the Overall Performance is always better than the observed performance since the observed DPMO is higher than the overall. However, we expect that the long term performance level to be lower than the short term by 1.5 sigma, which means that the long term DPMO should be higher than the observed NOT lower than the observed.

Anyone has any kind of explanation about this conflict? Maybe there is something I am not understanding well.

It’s late in the day but there will be tails reaching further out than an individual point for long term (expected overall) so that’s why you observe “observed ppm defective” always less than expected overall.

Sorry, but what I am saying is the reverse. The observed ppm is always MORE than the overall long term ppm, which is the reverse than expected, because I expect that performance reduces in the future and more ppm will occur than the current observed, which is not the case as displayed from the capability analysis output from minitab

What I did is to generate, say 200 records of normally distributed data for an output Diameter with mean 5.5 and sigma 0.5. Then, draw the capability analysis for this output with LSL=5.0 and USL=6.0. You will notice that observed ppm is much higher than overall ppm, which indicates that the long term performance is IMPROVING!!! which contradicts with the logic of 1.5 sigma shift, that the short term sigma level is higher than the long term.

However, when the process is now capable and all observed values are within the specification limits, NOW, the overall performance starts to indicate more ppm in the long term. So, I do not know why this is happening when the current process is not capable, but happening correctly when the process is capable!!!

It seems like not a rule :) Have you really got all the same 200 results? I’ve got another result from first attempt.

The explanation is following.

When process is capable, the Observed performance (PPM out of spec) is 0,00. While expected Overall performance will be always higher than zero (due to infinite Gaussian curve).

When process is not capable, it depends from data distribution. It cannot be ideally normal and symmetric. Some part of data is more in left or right side and PPM < LSL and PPM > USL would be different for any (e.g. 200) amount of generated distributions with the same target and std. Hence, for not capable process Overall and Observed performance might be higher or lower each other (actually not dramatically if it is not crazy skewed or uniform distribution).